Got through the main part fine, code worked, no major issues.
Start by clarifying the problem and constraints, then outline a simple solution and code it. After validating the simple solution, discuss how to extend it to the follow-up case, focusing on scalability, reliability, and trade-offs relevant to DevOps.
Pro tip: Demonstrate DevOps mindset by discussing how the solution would be deployed, monitored, and scaled in production, and mention automation and infrastructure as code.
Ask questions to understand the problem scope, input/output, constraints, and what the follow-up case entails. Confirm assumptions before proceeding.
Propose a straightforward algorithm, discuss its time and space complexity, and write pseudocode or actual code. Explain your reasoning.
Walk through test cases, including edge cases, to ensure correctness. Mention how you would unit test the script.
Analyze the follow-up case, identify additional requirements (e.g., scalability, concurrency, fault tolerance), and modify the solution accordingly. Discuss trade-offs.
Explain how you would deploy, monitor, and maintain the script in a DevOps context, including CI/CD, logging, and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose one subsystem you know deeply, such as memory management or process scheduling, and explain its core mechanisms and design trade-offs. Connect your explanation to DevOps concerns like performance tuning, observability, and reliability at scale. Use concrete examples from your experience to demonstrate practical understanding.
Pro tip: Tie the subsystem to real-world DevOps scenarios, like diagnosing OOM kills or tuning CPU schedulers for latency-sensitive workloads, to show you understand its operational impact. Mentioning tools like perf, ftrace, or /proc interfaces signals hands-on maturity.
Pick one subsystem you can discuss confidently and briefly state its role in the kernel. Avoid trying to cover multiple subsystems superficially.
Describe the key data structures, algorithms, and flows (e.g., page allocation, CFS scheduling, block I/O queues). Keep it structured and avoid unnecessary jargon.
Discuss design decisions and their trade-offs, such as throughput vs. latency, fairness vs. efficiency, or memory overcommit vs. safety.
Relate the subsystem to operational tasks: monitoring, tuning, troubleshooting, and capacity planning. Mention relevant tools and metrics.
Conclude with how this knowledge helps you build and maintain reliable, high-performance systems in a DevOps context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Lean hire on this one, which stings a little because I thought it went okay.
Structure your answer as a logical, step-by-step narrative that mirrors a real incident response: start by acknowledging the symptom and impact, then move through detection, triage, investigation, and resolution. Emphasize data-driven decision-making and collaboration, and conclude with preventive measures to show a holistic DevOps mindset.
Pro tip: Google values blameless postmortems and systemic fixes, so highlight how you'd use tools like distributed tracing (e.g., Dapper) and logging (e.g., Cloud Logging) to pinpoint root cause without finger-pointing. Also, mention how you'd communicate status updates to stakeholders throughout the process.
Confirm the symptom, its scope, and user impact. Check dashboards and alerts to understand severity and affected systems.
Take immediate steps to mitigate (e.g., rollback, failover) while collecting logs, metrics, and traces. Preserve evidence for root cause analysis.
Based on data, hypothesize potential causes (e.g., recent deploy, config change, resource exhaustion). Validate or eliminate each through targeted queries or experiments.
Pinpoint the underlying issue using techniques like the 5 Whys or fault tree analysis. Implement a fix and verify resolution.
Conduct a blameless postmortem, add monitoring/alerting, and implement long-term fixes (e.g., automation, capacity planning). Share learnings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around a systematic troubleshooting methodology, starting from the user-facing symptoms and working down the stack. Emphasize observability, hypothesis-driven investigation, and the importance of understanding the system's architecture and dependencies.
Pro tip: Mention that you would first check if the issue is widespread or isolated, and use distributed tracing to pinpoint the failing component. Also, highlight the importance of documenting findings and conducting a post-mortem to prevent recurrence.
Clarify the symptoms, affected users/services, and timeline. Determine if the issue is isolated to a specific region, service, or component.
Collect metrics, logs, and traces from relevant components. Use monitoring tools to identify anomalies and establish a baseline.
Based on the data, hypothesize potential root causes (e.g., network partition, DNS failure, misconfiguration). Test each hypothesis methodically, isolating variables.
Once the root cause is identified, apply a targeted fix. Verify that the issue is resolved and monitor for any side effects.
Document the incident, root cause, and resolution. Update runbooks and implement preventive measures (e.g., alerts, automation) to avoid recurrence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to structure your answer, focusing on a specific situation where ambiguity or conflict arose. Highlight how you clarified the problem, collaborated with the team, and drove a resolution while demonstrating DevOps principles like automation, monitoring, and blameless culture. Emphasize the positive outcome and what you learned.
Pro tip: Show that you can turn ambiguity into actionable plans by breaking down problems and using data to drive decisions. At Google, emphasize how you leveraged automation and monitoring to reduce uncertainty and foster collaboration.
Briefly describe the team, project, and the source of ambiguity or conflict. Be specific about your role and the stakes involved.
Explain how you gathered information, identified root causes, and aligned the team on a shared understanding of the issue.
Detail the steps you took to resolve the ambiguity or conflict, including collaboration, automation, or process improvements.
Describe how you used metrics or feedback to validate the solution and adjust as needed.
Summarize the outcome, what you learned, and how it improved your approach to similar situations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.